rm(list = ls())
library(data.table)
library(plyr)
library(tidyr)
library(lfe)
library(stargazer)
library(xtable)
library(sandwich)
library(roll)
library(readxl)
library(readr)
library(zoo)
library(texreg)
library(DescTools)
library(ggplot2)

m <- data.table(read_xlsx("../Data/MasterData.xlsx", skip = 1, guess_max = 1e4))
m[, age := 2021 - year]
#
m[, date := as.Date(date, "%d%b%Y")]
m[, dayofweek := wday(date)]

m[, memrableperiod_pret := memrableperiod_pret * 100]
m[, memrableperiod_aret := memrableperiod_aret * 100]
m[, bias := (memrableperiod_pret - memrableperiod_aret)]

m[, retrecall_1day_rate := retrecall_1day_rate * 100]
m[, retrecall_30day_rate := retrecall_30day_rate * 100]
m[, retrecall_1year_rate := retrecall_1year_rate * 100]
m[, retrecall_5year_rate := retrecall_5year_rate * 100]

m[, exretrecall_1day_rate := retrecall_1day_rate - ret_lagd1 * 100]
m[, exretrecall_1year_rate := retrecall_1year_rate - ret_lagy1 * 100]

m[, expret30day_market_rate := expret30day_market_rate * 100]
m[, expret1year_market_rate := expret1year_market_rate * 100]
m[, expret30day_self_rate := expret30day_self_rate * 100]
m[, expret1year_self_rate := expret1year_self_rate * 100]

###########################################################################
# Free Recall
###########################################################################

m[, ret_intraday2 := ret_intraday2 * 100]
m[, ret_lagmonth := (ret_lagw1 + ret_lagw2 + ret_lagw3 + ret_lagw4) * 100]
m[, exretrecall_30day_rate := retrecall_30day_rate - ret_lagmonth]

m[, memrableperiod_begin := as.yearmon(memrableperiod_begin, "%Ym%m")]
m[, memrableperiod_end := as.yearmon(memrableperiod_end, "%Ym%m")]
m[, memrableperiod_max := max(memrableperiod_end)]
m[, memrableperiod_dist := as.yearmon(2022 + 2 / 12) - (memrableperiod_begin + memrableperiod_end) / 2]

m[, media_cue := log(word_count_up_intraday / word_count_down_intraday)]


sample <- m[type == 0 & memrableperiod_end < "Dec 2020"] # at least one year before the end of sample

# 5 years
sample <- m[type == 0 & memrableperiod_end >= memrableperiod_max - 5 &
  memrableperiod_end < "Dec 2020" & !is.na(ret_intraday2 + ret_lagmonth)]

f1 <- felm(memrableperiod_pret ~ media_cue + ret_intraday2 + ret_lagmonth | age + gender + education + total_wealth + total_income +
  accountcheck_freq + newscheck_freq + discussion_freq + num_wechat | 0 | date, sample)

sample1 <- m[dayofweek >= 2 & dayofweek <= 6 & 
  !is.na(exretrecall_1day_rate) & !is.na(exretrecall_30day_rate) & 
  !is.na(ret_intraday2)]

f2 <- felm(exretrecall_1day_rate ~ media_cue + ret_intraday2 + ret_lagmonth  | age + gender + education + total_wealth + total_income +
  accountcheck_freq + newscheck_freq + discussion_freq + num_wechat | 0 | date, sample1)

f3 <- felm(exretrecall_30day_rate ~ media_cue + ret_intraday2 + ret_lagmonth  | age + gender + education + total_wealth + total_income +
  accountcheck_freq + newscheck_freq + discussion_freq + num_wechat | 0 | date, sample1)

stargazer(f1, f2, f3,
  align = TRUE, dep.var.labels.include = TRUE,
  covariate.labels = c("MediaCue\\textsubscript{today}", "MktRet\\textsubscript{today}", "MktRet\\textsubscript{1M}"),
  omit.stat = c("LL", "ser", "F", "rsq"), ord.intercepts = FALSE, no.space = TRUE,
  title = "",
  single.row = FALSE, column.sep.width = "0pt", digits = 2
)
